This repository contains a pytorch code to replicate experiments in the paper: Learning and Generalization in Overparameterized Normalizing Flows.
The code for synthetic datasets is tested on
- python 3.6.9
- pytorch 1.10.1
- matplotlib 3.3.4
- numpy 1.19.5
- Experiments of the paper is divided in two parts. Code files for Constrained Normalizing Flows (CNFs) are given in
./CNF/
folder. Code files for Unconstrained Normalizing Flows (UNFs) are given in./UNF/
folder. - All synthetic datasets used in the paper are given in
./datasets/
folder. - Code to reproduce the results for the Miniboone dataset is given in
./BNAF/
and./UMNN/
.
For experiments on Miniboone datasets, we use the code from BNAF and UMNN.
If you find this project useful, please consider citing the following publication:
@article{shah2021learning,
title={Learning and Generalization in Overparameterized Normalizing Flows},
author={Shah, Kulin and Deshpande, Amit and Goyal, Navin},
journal={arXiv preprint arXiv:2106.10535},
year={2021}
}